Arabic Aspect-Based Sentiment Analysis: A Systematic Literature Review

Obiedat, Ruba; Al-Darras, Duha; Alzaghoul, Esra; Harfoushi, Osama

doi:10.1109/access.2021.3127140

Cited by 43 publications

(18 citation statements)

References 44 publications

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“…By combining all of the user's review texts for a given item, our suggested model generates a unique matrix based on language frequency. Similar to that, a device's review is regarded as all assessment text for that product gathered from relevant users [5]. A strategy that combines memory networks and attention mechanisms IET Commun.…”

Section: Introductionmentioning

confidence: 99%

Retracted: Aspect‐based sentiment analysis employing linguistics content over social media for Web of Things

Jindal¹,

Kumar²,

Kaushal

et al. 2022

IET Communications

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The above article from IET Communications, published online on 12 November 2022 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Interim Editor‐in‐Chief, Jian Ren, the Institution of Engineering and Technology (the IET) and John Wiley and Sons Ltd. This article was published as part of a Guest Edited special issue. Following an investigation, the IET and the journal have determined that the article was not reviewed in line with the journal's peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. Accordingly, we cannot vouch for the integrity or reliability of the content. As such we have taken the decision to retract the article. The authors have been informed of the decision to retract.

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Section: Introductionmentioning

confidence: 99%

Retracted: Aspect‐based sentiment analysis employing linguistics content over social media for Web of Things

Jindal¹,

Kumar²,

Kaushal

et al. 2022

IET Communications

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“…They provided an overview of specific sentiment analysis tasks and of the features and methods required for different tasks (Ligthart et al 2021). Obiedat (Obiedat et al 2021), Angel (Angel et al 2021) and Lin (Lin et al 2022) also all followed this guideline to select literature for further analysis. This method can reduce the amount of literature that requires in-depth reading, but in the case of a large amount of literature, more effort is still required to search and screen the material than in traditional literature review methods (Kitchenham and Charters 2007).…”

Section: Overall Survey Methodologymentioning

confidence: 99%

“…Comparing and analyzing papers from specific disciplines can help researchers gain a comprehensive understanding of the field. There have been many surveys on sentiment analysis (Nair et al 2019;Obiedat et al 2021;Raghuvanshi and Patil 2016). However, there is a lack of adequate discussion on the connections between research methods and topics in the field, as well as on their evolution over time.…”

Section: Introductionmentioning

confidence: 99%

“…Table1, the content analysis method can effectively analyze the contents of research papers in depth, but it does not address the issue of the evolution of the research methods and topics(Bengtsson 2016;Birjali et al 2021;Elo and Kyngäs 2008;Krippendorff 2018;Qazi et al 2015Qazi et al , 2017Wankhade et al 2022). Although the number of papers that need to be read in depth can be reduced by following Kitchenham and Charters' guideline, more effort is needed to search and screen literature than in traditional literature review methods(Angel et al 2021;Kitchenham 2004;Kitchenham and Charters 2007;Kumar and Garg 2020;Ligthart et al 2021;Lin et al 2022;Obiedat et al 2021;Sarsam et al 2020;Zunic et al 2020). The informetric methods are best suited to investigating the research methods and topics of sentiment analysis(Bar-Ilan 2008;Mäntylä et al 2018;Piryani et al 2017;Santos et al 2019;Verma …”

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confidence: 99%

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Survey on sentiment analysis: evolution of research methods and topics

Cui

Wang

et al. 2023

Artif Intell Rev

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Sentiment analysis, one of the research hotspots in the natural language processing field, has attracted the attention of researchers, and research papers on the field are increasingly published. Many literature reviews on sentiment analysis involving techniques, methods, and applications have been produced using different survey methodologies and tools, but there has not been a survey dedicated to the evolution of research methods and topics of sentiment analysis. There have also been few survey works leveraging keyword cooccurrence on sentiment analysis. Therefore, this study presents a survey of sentiment analysis focusing on the evolution of research methods and topics. It incorporates keyword co-occurrence analysis with a community detection algorithm. This survey not only compares and analyzes the connections between research methods and topics over the past two decades but also uncovers the hotspots and trends over time, thus providing guidance for researchers. Furthermore, this paper presents broad practical insights into the methods and topics of sentiment analysis, while also identifying technical directions, limitations, and future work.

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“…According to the analysis granularity, text sentiment analyses can be divided into document-level sentiment analysis, sentence-level sentiment analysis, and aspect-based sentiment analysis. Among them, Aspect-Based Sentiment Analysis (ABSA) [1][2][3] is the most complex, because it needs to understand the semantic information at word and sentence levels, which requires not only the analysis of explicit language expression structure but also an understanding of implicit semantic expression. The ABSA task consists of three basic subtasks: Aspect Term Extraction (ATE) [4][5][6], Opinion Term Extraction (OTE), [7,8], and Aspect Sentiment Classification (ASC) [9][10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

A Multi-Task Dual-Encoder Framework for Aspect Sentiment Triplet Extraction

Huan

Xie

et al. 2022

IEEE Access

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Aspect Sentiment Triplet Extraction (ASTE) is a complex and important task in aspect-based sentiment analysis task, which aims to extract aspect-sentiment-opinion triplets from review sentences, to acquire comprehensive information for sentiment analysis. Most of the existing methods use pipeline approaches or end-to-end sequence tagging approaches to solve the ASTE task. However, the pipeline approaches suffer from error accumulation in practical applications. The existing sequence tagging approaches ignore the feature information of the three elements themselves, and it is impossible to model and infer the three elements effectively by placing each word in the same position as importance. Based on this, a multitask dual-encoder framework is proposed. First, a dual-encoder is constructed to encode and fuse sentence information and semantic information, respectively. Then, the signs and constraints implied between word pairs are used to complete multi-task inference and triplet decoding. Meanwhile, we design two grid tagging methods and their corresponding inference strategies, respectively, for two tasks. The auxiliary task is used as a regularization of the main task, which improves the correct inference ability of the inference strategy for the main task and the robustness of the framework. Extensive testing on two benchmark datasets shows that the proposed framework is simple and effective, and significantly outperforms existing methods.INDEX TERMS Aspect based sentiment analysis, Aspect sentiment triplet extraction, Natural language processing.

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Arabic Aspect-Based Sentiment Analysis: A Systematic Literature Review

Cited by 43 publications

References 44 publications

Retracted: Aspect‐based sentiment analysis employing linguistics content over social media for Web of Things

Retracted: Aspect‐based sentiment analysis employing linguistics content over social media for Web of Things

Survey on sentiment analysis: evolution of research methods and topics

A Multi-Task Dual-Encoder Framework for Aspect Sentiment Triplet Extraction

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